Teaching & Outreach

Short courses

Machine Learning for Solid Mechanics

Offerred in: 

  1. WCCM 2024 / PANACM 2024, 16th World Congress on Computational Mechanics, Vancouver, British Columbia, Canada, July 2024. [Link]
  2. USNCCM 17, 17th U.S. National Congress on Computational Mechanics, Albuquerque, New Mexico, July 2023. 
    • The slides of my lab session can be found here.

Description: This course will be offered to graduate students and researchers to introduce the practical data analytics, dimension reduction, and machine learning techniques for a variety of science and engineering applications in materials, structures, and systems.This course is designed for the audience with a background in mechanics and/or applied physics. The course will overview four major categories of machine learning techniques (dimensional reduction of manifold data, generative artificial intelligence (denoising diffusion probabilistic models) and symbolic regression ad knowledge graph for interpretable scientific machine learning. Case studies will demonstrate how these learning techniques have enhanced research and technology advancements. These application problems will include a data-driven model-free paradigm for complex material systems, reduced-order modeling of fracture and thermal fatigue analysis, geometric learning for polycrystal and energetic materials. Lecture materials and lab handouts will be provided before the short course.

USNCCM Short Course

2023.07.23 - Qizhi giving a short course lecture at USNCCM 2023. Special thanks to Kristen Susuki (UC San Diego) for being the Lab Assistant